Scaling AI Solutions In Enterprises

To build enterprise-scale, production-ready AI agents, we need more than just a large language model (LLM). We need a full ecosystem. That’s exactly what this AI Agent System Blueprint lays out. 🔹 1. Input/Output – Flexible User Interaction Agents today must go beyond text. They take multimodal inputs—documents, images, audio, even video—so users can interact naturally and contextually. 🔹 2. Orchestration – The Nervous System Frameworks like LangGraph, Guardrails, Google ADK sit at the orchestration layer. They handle: Context management Streaming & tracing Deployment and evaluation Guardrails for safety & compliance Without orchestration, agents remain fragile demos. With it, they become scalable and reliable. 🔹 3. Data and Tools – Context is Power Agents get smarter when connected to enterprise data: Vector & semantic DBs Internal knowledge bases APIs from Stripe, Slack, Brave, and beyond This ensures every decision is grounded in context, not hallucination. 🔹 4. Reasoning – Brains of the System Multiple model types collaborate here: LLMs (Gemini Flash, GPT-4o, DeepSeek R1) SLMs (Gemma, PiXtral 12B) for lightweight use cases LRMs (OpenAI o3, DeepSeek) for specialized reasoning Agents analyze prompts, break them down, and decide which tools or APIs to call. 🔹 5. Agent Interoperability – Teams of Agents No single agent does it all. Using protocols like MCP, multiple agents—Sales Agent, Docs Agent, Support Agent—communicate and collaborate seamlessly. This is where multi-agent ecosystems shine. Why This Blueprint Matters When you combine these layers, you get AI agents that: ✅ Adapt to any input ✅ Make reliable decisions with enterprise context ✅ Collaborate like real teams ✅ Scale safely with guardrails and orchestration This is how we move from fragile prototypes → production-ready agent ecosystems. The big question: Which layer do you see as the hardest bottleneck for enterprises—Orchestration, Reasoning, or Data & Tools?

In January, everyone signs up for the gym, but you're not going to run a marathon in two or three months. The same applies to AI adoption. I've been watching enterprises rush into AI transformations, desperate not to be left behind. Board members demanding AI initiatives, executives asking for strategies, everyone scrambling to deploy the shiniest new capabilities. But here's the uncomfortable truth I've learned from 13+ years deploying AI at scale: Without organizational maturity, AI strategy isn’t strategy — it’s sophisticated guesswork. Before I recommend a single AI initiative, I assess five critical dimensions: 1. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Can your systems handle AI workloads? Or are you struggling with basic data connectivity? 2. 𝗗𝗮𝘁𝗮 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: Is your data accessible? Or scattered across 76 different source systems? 3. 𝗧𝗮𝗹𝗲𝗻𝘁 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Do you have the right people with capacity to focus? Or are your best people already spread across 14 other strategic priorities? 4. 𝗥𝗶𝘀𝗸 𝘁𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲: Is your culture ready to experiment? Or is it still “measure three times, cut once”? 5. 𝗙𝘂𝗻𝗱𝗶𝗻𝗴 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Are you willing to invest not just in tools, but in the foundational capabilities needed for success? This maturity assessment directly informs which of five AI strategies you can realistically execute: - Efficiency-based - Effectiveness-based - Productivity-based - Growth-based - Expert-based Here's my approach that's worked across 39+ production deployments: Think big, start small, scale fast. Or more simply: 𝗖𝗿𝗮𝘄𝗹. 𝗪𝗮𝗹𝗸. 𝗥𝘂𝗻. The companies stuck in POC purgatory? They sprinted before they could stand. So remember: AI is a muscle that has to be developed. You don't go from couch to marathon in a month, and you don't go from legacy systems to enterprise-wide AI transformation overnight. What's your organization's AI fitness level? Are you crawling, walking, or ready to run?

The new Gartner Hype Cycle for AI is out, and it’s no surprise what’s landed in the trough of disillusionment… Generative AI. What felt like yesterday’s darling is now facing a reality check. Sky-high expectations around GenAI’s transformational capabilities, which for many companies, the actual business value has been underwhelming. Here’s why.… Without solid technical, data, and organizational foundations, guided by a focused enterprise-wide strategy, GenAI remains little more than an expensive content creation tool. This year’s Gartner report makes one thing clear... scaling AI isn’t about chasing the next AI model or breakthrough. It’s about building the right foundation first. ☑️ AI Governance and Risk Management: Covers Responsible AI and TRiSM, ensuring systems are ethical, transparent, secure, and compliant. It’s about building trust in AI, managing risks, and protecting sensitive data across the lifecycle. ☑️ AI-Ready Data: Structured, high-quality, context-rich data that AI systems can understand and use. This goes beyond “clean data”, we’re talking ontologies, knowledge graphs, etc. that enable understanding. “Most organizations lack the data, analytics and software foundations to move individual AI projects to production at scale.” – Gartner These aren’t nice-to-haves. They’re mandatory. Only then should organizations explore the technologies shaping the next wave: 🔷 AI Agents: Autonomous systems beyond simple chatbots. True autonomy remains a major hurdle for most organizations. 🔷 Multimodal AI: Systems that process text, image, audio, and video simultaneously, unlocking richer, contextual understanding. 🔷 TRiSM: Frameworks ensuring AI systems are secure, compliant, and trustworthy. Critical for enterprise adoption. These technologies are advancing rapidly, but they’re surrounded by hype (sound familiar?). The key is approaching them like an innovator...  start with specific, targeted use cases and a clear hypothesis, adjusting as you go. That’s how you turn speculative promise into practical value. So where should companies focus their energy today? Not on chasing trends, but on building the capacity to drive purposeful innovation at scale: 1️⃣ Enterprise-wide AI strategy: Align teams, tech, and priorities under a unified vision 2️⃣ Targeted strategic use cases: Focus on 2–3 high-impact processes where data is central and cross-functional collaboration is essential. 3️⃣ Supportive ecosystems: Build not just the tech stack, but the enablement layer, training, tooling, and community, to scale use cases horizontally. 4️⃣ Continuous innovation: Stay curious. Experiment with emerging trends and identify paths of least resistance to adoption. AI adoption wasn’t simple before ChatGPT, and its launch didn’t change that. The fundamentals still matter. The hype cycle just reminds us where to look. Gartner Report:  https://lnkd.in/g7vKc9Vr #AI #Gartner #HypeCycle #Innovation

Generative AI is a complete set of technologies that work together to provide intelligence at scale. This stack includes the foundation models that create text, images, audio, or code. It also features production monitoring and observability tools that ensure systems are reliable in real-world applications. Here’s how the stack comes together: 1. 🔹Foundation Models At the base, we have models trained on large datasets, covering text (GPT, Mistral, Anthropic), audio (ElevenLabs, Speechify, Resemble AI), 3D (NVIDIA, Luma AI, Open Source), image (Stability AI, Midjourney, Runway, ClipDrop), and code (Codium, Warp, Sourcegraph). These are the core engines of generation. 2. 🔹Compute Interface To power these models, organizations rely on GPU supply chains (NVIDIA, CoreWeave, Lambda) and PaaS providers (Replicate, Modal, Baseten) that provide scalable infrastructure. Without this computing support, modern GenAI wouldn’t be possible. 3. 🔹Data Layer Models are only as good as their data. This layer includes synthetic data platforms (Synthesia, Bifrost, Datagen) and data pipelines for collection, preprocessing, and enrichment. 4. 🔹Search & Retrieval A key component is vector databases (Pinecone, Weaviate, Milvus, Chroma) that allow for efficient context retrieval. They power RAG (Retrieval-Augmented Generation) systems and keep AI responses grounded. 5. 🔹ML Platforms & Model Tuning Here we find training and fine-tuning platforms (Weights & Biases, Hugging Face, SageMaker) alongside data labeling solutions (Scale AI, Surge AI, Snorkel). This layer helps models adjust to specific domains, industries, or company knowledge. 6. 🔹Developer Tools & Infrastructure Developers use application frameworks (LangChain, LlamaIndex, MindOS) and orchestration tools that make it easier to build AI-driven apps. These tools connect raw models and usable solutions. 7. 🔹Production Monitoring & Observability Once deployed, AI systems need supervision. Tools like Arize, Fiddler, Datadog and user analytics platforms (Aquarium, Arthur) track performance, identify drift, enforce firewalls, and ensure compliance. This is where LLMOps comes in, making large-scale deployments reliable, safe, and clear. The Generative AI Stack turns raw model power into practical AI applications. It combines compute, data, tools, monitoring, and governance into one seamless ecosystem. #GenAI

Big consulting firms rushing to AI...do better. In the rapidly evolving world of AI, far too many enterprises are trusting the advice of large consulting firms, only to find themselves lagging behind or failing outright. As someone who has worked closely with organizations navigating the AI landscape, I see these pitfalls repeatedly—and they’re well documented by recent research. Here is the data: 1. High Failure Rates From Consultant-Led AI Initiatives A combination of Gartner and Boston Consulting Group (BCG) data demonstrates that over 70% of AI projects underperform or fail. The finger often points to poor-fit recommendations from consulting giants who may not understand the client’s unique context, pushing generic strategies that don’t translate into real business value. 2. One-Size-Fits-All Solutions Limit True Value Boston Consulting Group (BCG) found that 74% of companies using large consulting firms for AI encounter trouble when trying to scale beyond the pilot phase. These struggles are often linked to consulting approaches that rely on industry “best practices” or templated frameworks, rather than deeply integrating into an enterprise’s specific workflows and data realities. 3. Lost ROI and Siloed Progress Research from BCG shows that organizations leaning too heavily on consultant-driven AI roadmaps are less likely to see genuine returns on their investment. Many never move beyond flashy proof-of-concepts to meaningful, organization-wide transformation. 4. Inadequate Focus on Data Integration and Governance Surveys like Deloitte’s State of AI consistently highlight data integration and governance as major stumbling blocks. Despite sizable investments and consulting-led efforts, enterprises frequently face the same roadblocks because critical foundational work gets overshadowed by a rush to achieve headline results. 5. The Minority Enjoy the Major Gains MIT Sloan School of Management reported that just 10% of heavy AI spenders actually achieve significant business benefits—and most of these are not blindly following external advisors. Instead, their success stems from strong internal expertise and a tailored approach that fits their specific challenges and goals.

The secret to 10x impact from AI is changing *what* work you do, not only how your team does that work. See AI as more than a “productivity tool.” To succeed and become executives, leaders must think of AI differently than coders, designers, PMs, and other ICs. Here is how to *lead* with AI: It can be used to do things faster or more easily, but that isn’t where the real opportunity is. The real opportunity for leaders to grow their careers using AI is by using it to create net new value for the company: new products, better margins, or systems that fundamentally reduce cost or complexity. Creating new value is what will win you new opportunities, responsibilities, and eventually, a promotion. Using AI to do this requires knowledge and experience with AI tools and applications, a clear strategy, and the leadership skill to guide the process. Here’s how I would go about gaining that knowledge, creating the strategy, and leading the change in my organization: First, I’d deeply engage with AI. I would set aside time to personally test tools, follow AI experts, attend workshops, and build a mental model of where AI can create real leverage in my organization. I would also ask my team where they are currently using AI and what sort of results they are seeing. Second, I’d craft experiments. The leaders who will stand out will ask: what can we do now that we couldn’t do before? What cost structures can we eliminate? What customer problems can we solve in a new way? I would ask these questions and create hypotheses based on what I learned playing with tools and from others. I would then test these hypotheses with funded experiments that have meaningful but manageable impact. Third, I’d lead AI adoption by shaping culture. I'd ensure clarity on the “why” behind our AI efforts and I’d create a culture where experimentation is encouraged and failure is safe. I’d set expectations that we “use AI,” identify champions, and work with those who are resistant so that they feel supported in the change but also understand that it is a new expectation and not a request. The challenge with leading AI today is that it is already in your organization. Some are using it, others are opposing it and fearing it, everyone is aware of it. If you don’t lead your team through its use, you’ll lose control of it. Teams will adopt it unevenly, causing friction and confusion. On the flip side, if you lead well, it has the ability to 10x your impact and skyrocket your career. AI is not a tech problem for most leaders. It’s a change management problem. If you are a strategic, curious, and thoughtful leader you will be able to manage this change for the benefit of your team, your business, and your career. I write more about this in today’s newsletter for paid subscribers. I designed a 30-day AI Leadership Sprint and a number of other resources you can use to lead AI adoption in your org. Read the newsletter here: https://buff.ly/QMlF266 What's missing?

AI field note: Reducing the 'mean time to ah-ha' (MTtAh) is critical for driving AI adoption—and unlocking the value. When it comes to AI adoption, there's a crucial milestone: the "ah-ha moment." It's that instant of realization when someone stops seeing AI as just a smarter search tool and starts recognizing it as a reasoning and integration engine—a fundamentally new way of solving problems, driving innovation, and collaborating with technology. For me, that moment came when I saw an AI system not just write code but also deploy it, identify errors, and fix them automatically. In that instant, I realized AI wasn’t just about automation or insights—it was about partnership. A dynamic, reasoning collaborator capable of understanding, iterating, and executing alongside us. But these "ah-ha moments" don’t happen by accident. Systems like ChatGPT or Claude excel at enabling breakthroughs, but it really requires us to ask the right questions. That creates a chicken-and-egg problem: until users see what’s possible, they struggle to imagine what else is possible. So how do we help people get hands-on with AI, especially in enterprise organizations, without relying on traditional training? Here are some approaches we have tried at PwC: 🤖 AI "Hackathons" or Challenges: Host short, low-stakes events where employees can experiment with AI on real problems. For example, marketing teams could test AI for campaign ideas, while operations teams explore process automation. ⚙️ Sandbox Environments: Provide low-friction, risk-aware access to AI tools within a dedicated environment. Let users explore capabilities like text generation, workflow automation, or analytics without worrying about “messing something up.” 🚀 Pre-built Use Cases: Offer ready-to-use templates for specific challenges, such as drafting a client email, summarizing documents, or automating routine reports. Seeing results in action builds confidence and sparks creativity. At PwC we have a community prompt library available to everyone, making it easier to get started. 🧩 Embedded AI Mentors: Assign "AI champions" who can guide teams on applying AI in their work. This informal mentorship encourages experimentation without formal, structured training. We do this at PwC and it's been huge. ⚡️ Integrate AI into Existing Tools: Embed AI into everyday platforms (like email, collaboration tools, or CRM systems) so users can naturally interact with it during routine workflows. Familiarity leads to discovery. Reducing the mean time to ah-ha—the time it takes someone to have that transformative realization—is critical. While starting with familiar use cases lowers the barrier to entry, the real shift happens when users experience AI’s deeper capabilities firsthand.

If you are an AI engineer, thinking how to choose the right foundational model, this one is for you 👇 Whether you’re building an internal AI assistant, a document summarization tool, or real-time analytics workflows, the model you pick will shape performance, cost, governance, and trust. Here’s a distilled framework that’s been helping me and many teams navigate this: 1. Start with your use case, then work backwards. Craft your ideal prompt + answer combo first. Reverse-engineer what knowledge and behavior is needed. Ask: → What are the real prompts my team will use? → Are these retrieval-heavy, multilingual, highly specific, or fast-response tasks? → Can I break down the use case into reusable prompt patterns? 2. Right-size the model. Bigger isn’t always better. A 70B parameter model may sound tempting, but an 8B specialized one could deliver comparable output, faster and cheaper, when paired with: → Prompt tuning → RAG (Retrieval-Augmented Generation) → Instruction tuning via InstructLab Try the best first, but always test if a smaller one can be tuned to reach the same quality. 3. Evaluate performance across three dimensions: → Accuracy: Use the right metric (BLEU, ROUGE, perplexity). → Reliability: Look for transparency into training data, consistency across inputs, and reduced hallucinations. → Speed: Does your use case need instant answers (chatbots, fraud detection) or precise outputs (financial forecasts)? 4. Factor in governance and risk Prioritize models that: → Offer training traceability and explainability → Align with your organization’s risk posture → Allow you to monitor for privacy, bias, and toxicity Responsible deployment begins with responsible selection. 5. Balance performance, deployment, and ROI Think about: → Total cost of ownership (TCO) → Where and how you’ll deploy (on-prem, hybrid, or cloud) → If smaller models reduce GPU costs while meeting performance Also, keep your ESG goals in mind, lighter models can be greener too. 6. The model selection process isn’t linear, it’s cyclical. Revisit the decision as new models emerge, use cases evolve, or infra constraints shift. Governance isn’t a checklist, it’s a continuous layer. My 2 cents 🫰 You don’t need one perfect model. You need the right mix of models, tuned, tested, and aligned with your org’s AI maturity and business priorities. ------------ If you found this insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content ❤️

I built AI products. My best clients wanted the builder instead. Last week, I saw Gokul Rajaram's post on X that perfectly captured what I've been experiencing: "ENTERPRISE AI: BUILD AGENTS, NOT TOOLS" He noticed AI startups are pivoting from selling tools for building agents to actually building and running those agents for enterprises themselves. This hit home. Six months ago, our company was exclusively product-focused. We'd built sophisticated AI tooling that solved specific GTM problems. The platform was solid, but something interesting kept happening. Our churning customers all shared a pattern: they loved the tool's capabilities but struggled to integrate it into their broader strategy. They had the technology but lacked the expertise to maximize it. Meanwhile, our most successful customers kept asking for more. "Can you help with our sales strategy too?" "Would you look at our entire customer journey?" They valued our strategic value add as much as our technology. The signal was clear. We were selling hammers to people who needed houses. So we pivoted. We evolved from a pure product company to offering full-stack solutions—building and running AI agents while providing strategic guidance. Our original platform became just one component of our offering. This mirrors exactly what Gokul observed: "Enterprise AI defensibility and value creation might lie in the full-stack approach to building, running and evaluating agents. Almost consulting-ish." The transformation wasn't easy. I worried about scaling concerns. About being "just a service business." About losing our technology identity. But the market was telling us something important: the biggest barrier to AI adoption isn't technology—it's expertise. Enterprises don't have the talent density or specialized knowledge to implement complex AI workflows effectively. This expertise gap creates an opportunity for startups willing to be full-stack providers. You own the outcome, not just provide the tool. Revenue more than tripled since our pivot. Customer satisfaction is at an all-time high. And we're building better technology because we deeply understand implementation challenges. The irony? By becoming "more service-oriented," we've created greater defensibility than we had as a pure technology vendor. In emerging technology markets, your expertise in implementing solutions often creates more value than the technology itself. AI adoption is fundamentally about bridging capability gaps, not just providing tools. #startups #founders #growth #ai

“We cannot afford to get locked in.” This refrain is becoming a common one in enterprises choosing their AI stack, and it’s not just talk. It shows up in how leading companies are architecting their AI capabilities today: - Every model is evaluated based on task performance, not vendor promises. - Routing is dynamic. Workflows adapt in real time to whichever model performs best. - Vendor loyalty is gone. Replaced by a cold, relentless focus on output. - Architectures are designed from the ground up for fast swapping and zero lock-in. This isn’t a philosophical stance. It’s a survival mechanism. AI is evolving too quickly for any single provider, framework, or foundation model to be the long-term answer. The model that outperforms today might fall behind in 90 days. Waiting for quarterly vendor updates or retraining internal teams is a luxury that high-performing enterprises can no longer afford. This is what it means to go ruthlessly multi-model. But here’s the deeper shift. Optionality is no longer an inefficiency. It’s strategy. Historically, having multiple tools for the same task was seen as overhead, a sign of organizational bloat. That logic breaks in the AI era. Optionality now means resilience, speed, and adaptability. It’s what allows companies to move at the pace of AI innovation, not be buried by it. There are critical implications for enterprise architecture here: 1/ Composable AI stacks are table stakes. Companies need to assume they’ll be plugging in and out different models, modalities, and tools constantly. 2/ Evaluation becomes a core competency. The companies that win will be those who build internal muscle around rapid, constant model benchmarking. Understanding which models are best at what tasks, on what data, and for which teams. 3/ Procurement and compliance need to catch up. A fast-switching architecture demands fast-switching contracts. Traditional enterprise procurement cycles of 60 to 90 day reviews, annual renewals and so on... simply don’t work when models improve weekly. Legal, security, and compliance teams must modernize for speed without compromising safety. 4/ Performance-based routing is the new normal. Just like the best data centers route traffic to where it can be served fastest and cheapest, AI workloads will increasingly be routed to the model that delivers the best outcome per task. Model-native load balancing is on the horizon. The ones who embrace this shift are not just experimenting with AI. They are operationalizing it. ♻️ Repost to share these insights! ➕ Follow Armand Ruiz for more